Abstract PD11-04: A multi-feature AI-based solution for cancer diagnosis in breast biopsies: A prospective blinded multi-site clinical study

Abstract

Abstract Objective This study aimed to clinically validate the performance of a multi-feature AI-based solution on detection of invasive versus in situ carcinomas and in situ ductal carcinoma high grade from atypical ductal hyperplasia/low grade DCIS compared to rigorous ground truth (GT) established by multiple blinded expert pathologists in breast biopsies. Design Performance of the AI solution was prospectively tested on breast biopsies from two medical institutions in different geographies. AI results were compared against the ground truth (GT) established by consensus of two subspecialist breast pathologists. The study endpoints were detection of invasive carcinoma (IDC, ILC) and DCIS/ADH, including differentiating between different DCIS grades and ADH. ADH and DCIS Low Grade were pooled together because of similar clinical management when diagnosed on a biopsy. Results Six pathologists participated in the study and reported on 436 breast biopsies (841 H&E slides), including 156 invasive (including 31 rare subtypes), 135 DCIS/ADH and 145 benign diagnoses. The AI solution demonstrated high performance when compared with the GT with an AUC of 0.99 for the detection of invasive carcinoma (specificity and sensitivity of 93.6% and 95.5% respectively) and with AUC of 0.95 for the detection of DCIS/ADH. The AI solution differentiated well between subtypes/grades of invasive and in-situ cancers with an AUC of 0.97 for IDC vs. ILC and AUC of 0.92 for DCIS high grade vs. low grade/ADH, respectively. Only 11 (7%) cases had discrepancies on invasive diagnosis, 4 of these between invasive versus benign diagnosis encompassing one case on which the invasive component was only represented by rare lympho-vascular invasion, two cases of ILC (one with a diffuse pattern and the second in a case with granulomatous mastitis with multinucleated giant cells and hemosiderin-laden macrophages) and one rare case of tubular carcinoma surrounded by flat epithelial atypia and columnar cell lesions. Fifteen cases (11%) had discrepancies on DCIS/ADH diagnosis between the two specialist pathologists necessitating a third assessment by a specialist to establish GT. Six of these cases also necessitated a review on a multihead microscope to reach a consensus decision, since there was no majority even after 3 reviews. Conclusion This blinded multi-site study reports the successful clinical validation of a multi-feature AI-based solution in detecting and automatically imparting clinically relevant diagnostic parameters regarding invasive and in situ breast carcinoma, offering an important tool for computer-aided diagnosis in routine pathology practice. Citation Format: Anne Vincent-Salomon, Guillaume Bataillon, Alona Nudelman, Judith Sandbank, Anat Albrecht Shach, Lucie Thibault, Lilach Bien, Rachel Mikulinsky, Ira Krasnitsky, Ronen Heled, Chaim Linhart, Manuela Vecsler, Daphna Laifenfeld. A multi-feature AI-based solution for cancer diagnosis in breast biopsies: A prospective blinded multi-site clinical study [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD11-04.